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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Start by finding the earliest available source and checking whether the file has Content Credentials or other provenance records. Then compare the file with its source and account for its handling history. A detector can offer a clue, but neither a detector score nor missing credentials proves that media is authentic or fake. First establish what changed; then judge whether the change makes the presentation misleading.
What does “altered by AI” mean?
It can mean different things: a file may have been generated with AI, edited with an AI feature, changed with conventional editing tools, or presented without the context that explains what it shows. Those are not interchangeable findings. A photo can be cropped or enhanced without being AI-generated, and a genuine recording can be misleading if it is taken out of context.
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Separate two questions: What, if anything, changed? and Does that change or the way the file is presented mislead? Cropping, noise reduction, color correction, enhancement, recompression, copying, or storage can change a file without deceptive intent. NIST’s Examining Digital Media treats media as having a lifecycle, so a visible or audible difference alone does not establish who made it or why.
How to check a photo, video, or audio clip
- Find the earliest available source. Search for the original post, publisher, photographer, recording, or event. Ask who supplied the file and how they obtained it. A repost, on its own, tells you little about the file’s history. NIST’s Examining Digital Media advises considering both the source and how the media was supplied.
- Inspect provenance records if available. Look for a Content Credentials indicator or use a compatible verifier to inspect any associated record. Check what it says about the creator, creation method, and edits—not just whether a credential is present. The C2PA specification describes signed, tamper-evident records that can accumulate editing steps. The Content Credentials overview also describes embedded provenance, invisible watermarking, and digital fingerprinting as ways intended to help credentials stay associated with content across workflows.
- Treat missing records as unknown. C2PA adoption is opt-in. A credential might never have been added, or it might not be available in the copy you are examining. Its absence does not establish that a file is fake or unaltered.
- Ask for the source file and its handling history. Find out whether it was edited, enhanced, copied, recompressed, or stored in a way that may have changed its appearance or sound. If possible, compare the file under review with a higher-quality original. A difference between the two needs explanation; it does not, by itself, identify an AI edit.
- Use automated detectors as leads, not verdicts. Before relying on a tool, ask which media types and manipulation types it was tested against and whether it reports false-positive and false-negative rates. NIST’s Identity Proofing Requirements calls for testing with genuine and forged or manipulated media, documenting those error rates, and augmenting automated decisions with manual review. These requirements apply to identity-proofing providers; they are not a guarantee that consumer detector websites meet the same standard.
- Corroborate the file and assess its context. Compare the source’s account with independent information about the publisher, recording, or event. A detector result or provenance record is more useful when it can be considered alongside the file’s source and lifecycle.
What provenance records can—and cannot—tell you
Provenance is evidence about a file’s recorded history, not a universal authenticity stamp. C2PA’s Content Credentials: C2PA Technical Specification, version 2.3, describes records that can preserve existing provenance while recording subsequent changes. That can help show what a participating workflow recorded about creation and editing.
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Coverage depends on adoption and on the record being available in the copy examined. A credential does not mean every step in a file’s life is known, and a missing credential does not establish that no edits occurred. NIST’s Reducing Risks Posed by Synthetic Content reviews provenance and detection approaches while noting that the efficacy of many approaches has not been fully examined; it cautions against assuming universal identification of AI edits.
C2PA’s July 31, 2026 implementation guide describes a standardized vocabulary for creation and editing events and possible localization of AI modifications through regions of interest. These are capabilities described in implementation guidance, not features that every existing file or verifier necessarily supports.
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How to compare verification approaches
| Approach | What it examines | What to check before relying on it |
|---|---|---|
| Provenance inspection | Available records about a file’s creator, creation method, and edits. | Whether a record is present and what it actually covers. C2PA is opt-in, so no record is not a finding that the file is fake or unaltered. |
| Source and context checking | Where the file first appeared, who supplied it, and how it relates to the claimed event. | Whether the source is original or merely a repost, and whether independent context supports the presentation. |
| Automated detection | Patterns a tool uses to infer whether media may be generated or manipulated. | Which media and manipulation types were tested, whether error rates are documented, and whether the result can be reviewed by a person. |
| Manual review | The file, its available source material, and the claims made about it. | Whether the review can compare the file with a better-quality original and corroborate its history. NIST recommends manual review alongside automated decisions in its identity-proofing context. |
A score from one detector should not be generalized to every image, video, or audio format. NIST’s detector-testing guidance is specific to identity proofing; it does not certify ordinary consumer tools.
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How to interpret a result
- Credentials are present: Read the recorded events and edits. Treat them as evidence of the history represented in that record, not a blanket guarantee that the content or its presentation is truthful.
- Credentials are absent: The history is not established by credentials in that copy. The absence does not prove either authenticity or fakery.
- A detector flags the file: Treat the result as a reason to investigate. Check the detector’s tested media and manipulation types, look for error-rate information, and seek human review for consequential decisions.
- The file differs from an original: Ask how it was edited, enhanced, copied, compressed, or stored before concluding that the change was deceptive or AI-made.
- The source or event cannot be corroborated: State that the file’s origin or context is unverified. Do not convert uncertainty into a claim that the file is AI-generated.
When the answer matters, describe the uncertainty
For a low-stakes question, source and provenance checks may be enough to decide whether a file warrants further scrutiny. For a consequential judgment, such as accusing someone of fabrication, do not rely on a single detector result. Keep the original file if available, note where the examined copy came from, compare available versions, and get qualified human review. If the evidence cannot establish what changed, say so rather than presenting a guess as a finding.
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